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IEEE International Conference on Automated software engineering (ASE)

作者： Shuzheng Gao Xin-Cheng Wen Cuiyun Gao Wenxuan Wang Hongyu Zhang Michael R. Lyu School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong China School of Big Data and Software Engineering Chongqing University China

Pre-trained models of source code have gained widespread popularity in many code intelligence tasks. Recently, with the scaling of the model and corpus size, large language models have shown the ability of in-context learning (ICL). ICL employs task instructions and a few examples as demonstrations, and then inputs the demonstrations to the language models for making predictions. This new learning paradigm is training-free and has shown impressive performance in various natural language processing and code intelligence tasks. However, the performance of ICL heavily relies on the quality of demonstrations, e.g., the selected examples. It is important to systematically investigate how to construct a good demonstration for code-related tasks. In this paper, we empirically explore the impact of three key factors on the performance of ICL in code intelligence tasks: the selection, order, and number of demonstration examples. We conduct extensive experiments on three code intelligence tasks including code summarization, bug fixing, and program synthesis. Our experimental results demonstrate that all the above three factors dramatically impact the performance of ICL in code intelligence tasks. Additionally, we summarize our findings and provide takeaway suggestions on how to construct effective demonstrations, taking into account these three perspectives. We also show that a carefully-designed demonstration based on our findings can lead to substantial improvements over widely-used demonstration construction methods, e.g., improving BLEU-4, EM, and EM by at least 9.90%, 175.96%, and 50.81% on code summarization, bug fixing, and program synthesis, respectively.

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Imitation learning from purified demonstrations 24

Imitation learning from purified demonstrations

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Proceedings of the 41st International Conference on Machine Learning

作者： Yunke Wang Minjing Dong Yukun Zhao Bo Du Chang Xu School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence and Wuhan Institute of Data Intelligence Wuhan University China Department of Computer Science City University of Hong Kong China School of Computer Science Faculty of Engineering The University of Sydney Australia

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential noises in imperfect demonstrations first, and subsequently conduct imitation learning from these purified demonstrations. Motivated by the success of diffusion model, we introduce a two-step purification via diffusion process. In the first step, we apply a forward diffusion process to smooth potential noises in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal demonstration from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that the distance between the purified and optimal demonstration can be bounded. Empirical results on MuJoCo and RoboSuite demonstrate the effectiveness of our method from different aspects.

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DCML: Boosting Applying Experience of NILM with Dilated Convolution and Multi-Task Learning

DCML: Boosting Applying Experience of NILM with Dilated Conv...

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IEEE International Conference on Communications in China (ICCC)

作者： ZhangMengru Zhao Fan Wu Huaqing Wu Tong Liu Conghao Zhou Jun Ma Yongmin Zhang Feng Lyu School of Computer Science and Engineering Central South University Changsha China Department of Electrical and Software Engineering University of Calgary Calgary AB Canada Big Data Institute Central South University Changsha China Department of Electrical and Computer Engineering University of Waterloo Waterloo Ontario Canada Hunan Energy Big Data Intelligence Limited Changsha China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Non-intrusive load monitoring (NILM) is a promising approach for recognizing various electrical equipment energy usage patterns from aggregate load data. In this paper, we investigate the development of an NILM model to achieve high recognition accuracy, ensure a small model size for lightweight implementation, and enhance its adaptability to a wide range of appliance types. Specifically, we propose a framework named DCML, which employs dilated convolution to precisely control the receptive field, allowing efficient adjustments in feature extraction granularity to meet the specific requirements of different appliances. In addition, multi-task learning techniques are incorporated to allow simultaneous recognition of multiple appliances from a single model, saving model space while ensuring high recognition accuracy. Compared to the benchmark, our model reduces the recognition mean absolute error (MAE) by $36.8 \%, 16.8 \%$, and $43.8 \%$ for fridges, microwaves, and washing machines, respectively. Furthermore, the proposed DCML can significantly save the storage space of convolutional layers when simultaneously recognizing multiple appliances.

关键词： Adaptation models Accuracy Convolution Multitasking data models Stability analysis Robustness

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A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection

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Computers, Materials & Continua 2022年第10期73卷 719-733页

作者： Jie Chen Chengsheng Yuan Chen Cui Zhihua Xia Xingming Sun Thangarajah Akilan School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310053China Jiangsu Yuchi Blockchain Research Institute Nanjing210044China Department of Software Engineering Lakehead UniversityThunder BayON P7B 5E1Canada

Fingerprint identification systems have been widely deployed in many occasions of our daily ***,together with many advantages,they are still vulnerable to the presentation attack(PA)by some counterfeit *** address challenges from PA,fingerprint liveness detection(FLD)technology has been proposed and gradually attracted people’s *** vast majority of the FLD methods directly employ convolutional neural network(CNN),and rarely pay attention to the problem of overparameterization and over-fitting of models,resulting in large calculation force of model deployment and poor model *** at filling this gap,this paper designs a lightweight multi-scale convolutional neural network method,and further proposes a novel hybrid spatial pyramid pooling block to extract abundant features,so that the number of model parameters is greatly reduced,and support multi-scale true/fake fingerprint ***,the representation self-challenge(RSC)method is used to train the model,and the attention mechanism is also adopted for optimization during execution,which alleviates the problem of model over-fitting and enhances generalization of detection ***,experimental results on two publicly benchmarks:LivDet2011 and LivDet2013 sets,show that our method achieves outstanding detection results for blind materials and *** size of the model parameters is only 548 KB,and the average detection error of cross-sensors and cross-materials are 15.22 and 1 respectively,reaching the highest level currently available.

关键词： FLD lightweight multi-scale RSC blind materials

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SODU2-NET: a novel deep learning-based approach for salient object detection utilizing U-NET

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PeerJ Computer science 2025年 11卷 e2623-e2623页

作者： Abbas, Hyder Ren, Shen Bing Asim, Muhammad Hassan, Syeda Iqra El-Latif, Ahmed A. Abd State Key Laboratory of Public Big Data College of Computer Science and Technology Institute for Artificial Intelligence Guizhou University Guizhou Guiyang China School of Computer Science and Engineering Central South University Changsha China EIAS Data Science and Blockchain Laboratory College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou China Department of Electrical and Electronic Engineering British Malaysian Institute Universiti of Kuala Lumpur Kuala Lumpur Malaysia Software Engineering Department Sir Syed University of Engineering and Technology Karachi Pakistan Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt

Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. To address this challenge posed by complex backgrounds in salient object detection is crucial for advancing the field. This article proposes a novel deep learning-based architecture called SODU2-NET (Salient object detection U2-Net) for salient object detection that utilizes the U-NET base structure. This model addresses a gap in previous work that focused primarily on complex backgrounds by employing a densely supervised encoder-decoder network. The proposed SODU2-NET employs sophisticated background subtraction techniques and utilizes advanced deep learning architectures that can discern relevant foreground information when dealing with complex backgrounds. Firstly, an enriched encoder block with full feature fusion (FFF) with atrous spatial pyramid pooling (ASPP) varying dilation rates to efficiently capture multi-scale contextual information, improving salient object detection in complex backgrounds and reducing the loss of information during down-sampling. Secondly the block includes an attention module that refines the decoder, is constructed to enhances the detection of salient objects in complex backgrounds by selectively focusing attention on relevant features. This allows the model to reconstruct detailed and contextually relevant information, which is essential to determining salient objects accurately. Finally, the architecture has been improved by adding a residual block at the encoder end, which is responsible for both saliency prediction and map refinement. The proposed network is designed to learn the transformation between input images and ground truth, enabling accurate segmentation of salient object regions with clear borders and accurate prediction of fine structures. SODU2-NET is demonstrated to have superior performance in five public datasets, including DUTS, SOD, DUT OMRON, HKU-IS, PASCA

关键词： Object detection

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PolyLite: A Lightweight Transformer With Multi-Scale Attention for Colorectal Polyp Segmentation

PolyLite: A Lightweight Transformer With Multi-Scale Attenti...

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International Conference on software, Knowledge Information, Industrial Management and Applications (SKIMA)

作者： Khaled ELKarazle Valliappan Raman Caslon Chua Patrick Then Faculty of Engineering Computing and Science Swinburne University of Technology (Sarawak Campus) Sarawak Malaysia Department of Artificial Intelligence and Data Science Coimbatore Institute of Technology Tamil Nadu India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia

The urgent need for early detection and intervention in colorectal cancer highlights the importance of automating colorectal polyp detection. Despite the use of deep learning techniques for polyp detection from medical images, these methods often struggle with issues such as skewed data distribution and class imbalances. In this study, we introduce PolyLite, a lightweight transformer-based model designed to balance performance and efficiency. PolyLite features a custom SegFormer encoder that takes advantage of its lightweight design, and a decoder that uses a multi-scale attention network (MA-NET) to achieve an optimal balance between accuracy and efficiency. To improve accuracy and reduce false negatives, we incorporate a reverse attention layer that effectively counteracts input mask attention during training. This refocuses the model's efforts to better learn non-polyp regions. PolyLite undergoes a two-stage training process, beginning with pre-training on the ImageNet dataset and then fine-tuning on the Kvasir-SEG dataset. We conduct rigorous evaluations on three complex datasets: CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB. Empirical results demonstrate PolyLite's superiority over existing techniques, as evidenced by its performance in terms of Intersection over Union and Dice scores. This validates its effectiveness and scalability in polyp segmentation, making complex and computationally intensive architectures unnecessary.

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A Novel AI-based Hybrid Ensemble Segmentation CAD System for Lumber Spine Stenosis pathological Regions Using MRI Axial Images

A Novel AI-based Hybrid Ensemble Segmentation CAD System for...

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International Artificial Intelligence and data Processing Symposium (IDAP)

作者： Saied Salem Raza Mukhlis Oguzhan Katar Bilal Ertuğrul Ozal Yildirim Mugahed A. Al-Antari Department of Artificial Intelligence and Data Science College of AI Convergence Sejong University Seoul Korea Department of Software Engineering Faculty of Technology Firat University Elazig Turkey Department of Neurosurgery Faculty of Medicine Firat University Elazığ Turkey

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

Lumbar Spinal Stenosis (LSS) is a condition where the spinal canal in the lower back becomes narrower, resulting in pressure on the spinal cord and nearby nerves. To predict LSS, neurologists focus on four key regions in MRI axial scans: the Intervertebral Disc (IVD), Thecal Sac (TS), Posterior Element (PE), and the Area between the Anterior and Posterior (AAP) vertebrae. To delineate these regions, we present a novel hybrid ensemble deep learning CAD system for multi-class segmentation, encompassing data collection, preprocessing, automated hyperparameter optimization and selection, segmentation ensemble learning, and both qualitative and quantitative performance evaluation. The core of the proposed pipeline features two ensemble phases: (1) ensemble learning through feature space fusion of three distinct AI models (SwinUNETR, Attention Unet, and UNet++), and (2) ensemble learning via a stacking method of the feature maps outputs from the same AI models used in phase 1. The Mendeley benchmark dataset is employed to train and assess both CAD systems, while a private dataset from Firat University Hospital in Turkey is used for verification and validation. The best achieved results via FFA’s mean dice score is 93.1% and mean IoU is 87.76%. showing the usefulness of using ensemble methods to capture different features from the trained models.

关键词： Deep learning Solid modeling Image segmentation Pathology Hospitals Magnetic resonance imaging Stacking Benchmark testing Ensemble learning Artificial intelligence

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Simulating cyber security management: A gamified approach to executive decision making 22

Simulating cyber security management: A gamified approach to...

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Proceedings of the 37th IEEE/ACM International Conference on Automated software engineering

作者： Adam Tonkin William Kosasih Marthie Grobler Mehwish Nasim Fivecast Australia School of Computer Science The University of Adelaide Australia CSIRO's Data61 Australia Department of Computer Science and Software Engineering The University of Western Australia Australia and College of Science and Engineering Flinders University Australia

ISBN: (纸本)9781450394758

Executive managers are not all equipped with the cyber security expertise necessary to enable them to make business decisions that accurately represent the status and needs of the cyber security side of the business. Unfortunately, the lack of understanding between the business and cyber security domains contribute to structurally endorsed vulnerabilities within a business context, where either the business needs were considered without understanding the impact on cyber security, or alternatively, the cyber security needs were considered without fully understanding the impact this would have on the business strategy and financial stability. To combat this dilemma, a gamified approach to cyber security training for executives is proposed as a solution to not only minimise the realisation of cyber vulnerabilities within a business context, but also to improve business outcomes that are supported by cyber security measures. We developed a serious game software platform, Aurelius, to simulate an executive decision maker’s role in managing the everyday cyber security investment decisions, and linking that to business metrics to incorporate the business and cyber security understanding. Our game includes simulated cyber security attacks that would require the executive decision maker (the player) to respond appropriately. The algorithms underpinning our simulated cyber security game are a product of a complex systems approach, as this most accurately models an executive’s experience. In our design, we set up Aurelius to fulfil eight of the nine criteria specified for a state of the art serious game in the cyber security domain.

关键词： serious game decision making human centric cyber security situational awareness. socio-technical systems simulation

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eSROP Attack: Leveraging Signal Handler to Implement Turing-Complete Attack Under CFI Defense 1

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18th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2022

作者： Zhang, Tianning Cai, Miao Zhang, Diming Huang, Hao Department of Computer Science and Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer and Information Hohai University Nanjing China College of Computer Engineering Jiangsu University of Science and Technology Nanjing China

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Signal Return Oriented Programming (SROP) is a dangerous code reuse attack method. Recently, defense techniques have been proposed to defeat SROP attacks. In this paper, we leverage the signal nesting mechanism provided by current operating systems and propose a new variant of SROP attack called enhanced SROP (eSROP) attack. eSROP provides the ability of invoking arbitrary system calls, simulating Turing-complete computation, and even bypassing the fine-grained label-based CFI defense, without modifying the return address and instruction register in the signal frame. Because the signal returns to the interrupted instruction, the shadow stack defense can hardly detect our attack. Signal has strong flexibility which can interrupt the normal control flow. We leverage such flexibility to design a new code reuse attack. To evaluate eSROP, we perform two exploits on two real-world programs, namely Proftpd and Wu-ftpd. In our attacks, adversaries can invoke arbitrary system calls and obtain a root shell. Both attacks succeed within 10 min under strict system defense such as data execution prevention, address space layout randomization, and coarse-grained control flow integrity. © 2023, ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Network security

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Deformation-Aware Segmentation Network Robust to Motion Artifacts for Brain Tissue Segmentation using Disentanglement Learning

arXiv

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arXiv 2024年

作者： Jung, Sunyoung Choi, Yoonseok Al-Masni, Mohammed A. Jung, Minyoung Kim, Dong-Hyun Department of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science College of Software and Convergence Technology Sejong University Seoul Korea Republic of Cognitive Science Research Group Korea Brain Research Institute Daegu Korea Republic of

Motion artifacts caused by prolonged acquisition time are a significant challenge in Magnetic Resonance Imaging (MRI), hindering accurate tissue segmentation. These artifacts appear as blurred images that mimic tissue-like appearances, making segmentation difficult. This study proposes a novel deep learning framework that demonstrates superior performance in both motion correction and robust brain tissue segmentation in the presence of artifacts. The core concept lies in a complementary process: a disentanglement learning network progressively removes artifacts, leading to cleaner images and consequently, more accurate segmentation by a jointly trained motion estimation and segmentation network. This network generates three outputs: a motion-corrected image, a motion deformation map that identifies artifact-affected regions, and a brain tissue segmentation mask. This deformation serves as a guidance mechanism for the disentanglement process, aiding the model in recovering lost information or removing artificial structures introduced by the artifacts. Extensive in-vivo experiments on pediatric motion data demonstrate that our proposed framework outperforms state-of-the-art methods in segmenting motion-corrupted MRI scans. The code is available at https://***/SunYJhxppy/Multi-Net. © 2024, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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